Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 253B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.30 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $2.50 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.03 | — |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.15Google AI Studio | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.25Google AI Studio | — |
| $185Google list price | — | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 19% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 66.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 76% |
| Overview | ||
| Company | NVIDIA | |
| Release date | Apr 17 2025 | Apr 8 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 2.5 FlashvsClaude Haiku 5.5Llama Nemotron Ultra 253BvsClaude Haiku 5.5Gemini 2.5 FlashvsGPT-6.1 SolLlama Nemotron Ultra 253BvsGPT-6.1 SolGemini 2.5 FlashvsMuse Spark OpenLlama Nemotron Ultra 253BvsMuse Spark OpenGemini 2.5 FlashvsGrok 4.7Llama Nemotron Ultra 253BvsGrok 4.7Gemini 2.5 FlashvsDeepSeek-V4.1-Flash-1017Llama Nemotron Ultra 253BvsDeepSeek-V4.1-Flash-1017Gemini 2.5 FlashvsMistral Large 4Llama Nemotron Ultra 253BvsMistral Large 4Frequently asked questions
Gemini 2.5 Flash and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 2.5 Flash has a verified first-party API price: $0.30 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for Llama Nemotron Ultra 253B. Llama Nemotron Ultra 253B shipped 9 days before Gemini 2.5 Flash, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.5 Flash) vs 128k (Llama Nemotron Ultra 253B). Gemini 2.5 Flash is closed, while Llama Nemotron Ultra 253B is open weight.
Direct benchmark comparisons are unavailable — Gemini 2.5 Flash and Llama Nemotron Ultra 253B don't publish scores on any of the same benchmarks.